arXiv subjects

Sitt Min Oo

Publications and source records attributed to Sitt Min Oo.

3 recordsLinked to original sources

arXiv2026

Query-Specific Pruning of RML Mappings (Extended Version)

Current approaches for knowledge graph construction with RML focus on full RDF graph materialization without considering user queries, which is inefficient in dynamic query environments where often only a specific subset of the full graph is needed to answer a given query. This paper introduces an approach to prune RML mappings such that the resulting partially-materialized graph is still sufficient to answer a given SPARQL query completely. By evaluating the approach based on a well-know RML materialization benchmark, we show that such pruning significantly reduces both the materialization time and the size of the produced graph, while also noticeably reducing querying time

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arXiv2025

An Algebraic Foundation for Knowledge Graph Construction (Extended Version)

Although they exist since more than ten years already, have attracted diverse implementations, and have been used successfully in a significant number of applications, declarative mapping languages for constructing knowledge graphs from heterogeneous types of data sources still lack a solid formal foundation. This makes it impossible to introduce implementation and optimization techniques that are provably correct and, in fact, has led to discrepancies between different implementations. Moreover, it precludes studying fundamental properties of different languages (e.g., expressive power). To address this gap, this paper introduces a language-agnostic algebra for capturing mapping definitions. As further contributions, we show that the popular mapping language RML can be translated into our algebra (by which we also provide a formal definition of the semantics of RML) and we prove several algebraic rewriting rules that can be used to optimize mapping plans based on our algebra.

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arXiv2022

RMLStreamer-SISO: an RDF stream generator from streaming heterogeneous data

Stream-reasoning query languages such as CQELS and C-SPARQL enable query answering over RDF streams. Unfortunately, there currently is a lack of efficient RDF stream generators to feed RDF stream reasoners. State-of-the-art RDF stream generators are limited with regard to the velocity and volume of streaming data they can handle. To efficiently generate RDF streams in a scalable way, we extended the RMLStreamer to also generate RDF streams from dynamic heterogeneous data streams. This paper introduces a scalable solution that relies on a dynamic window approach to generate RDF streams with low latency and high throughput from multiple heterogeneous data streams. Our evaluation shows that our solution outperforms the state-of-the-art by achieving millisecond latency (compared to seconds that state-of-the-art solutions need), constant memory usage for all workloads, and sustainable throughput of around 70,000 records/s (compared to 10,000 records/s that state-of-the-art solutions take). This opens up the access to numerous data streams for integration with the semantic web.

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